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The rapid development of automated vehicles has attracted a lot of attentions from the public in recent years. Current studies on automated vehicles mainly focus on microscopic simulations with simple network topologies and driver behaviors, and few has considered to incorporate automated vehicles into macroscopic travel demand models for the analysis in a regional network. In this project, we propose a multiple-resolution approach that allows us to model the impacts of automated vehicles for both transportation and traffic operation analysis. The approach hinges on the development of a capacity adjustment factor (CAF) for automated vehicles, similar to the heavy vehicles adjustment factor used in highway capacity analysis. CAF will be linked to input variables such as roadway facility types, traffic demand levels, and market penetration rates of automated vehicles. CAF will be derived from a microsimulation study, which involves the development of an integrated car-following model for both human drivers and automated vehicles, calibration of the model using NGSIM data, implementation of the model as a plugin in microsimulation. The proposed modeling approach can then be used to analyze the impact of automated vehicles in a regional network through additional traffic assignment runs using the adjusted capacity based on CAF.

The rapid development of automated vehicles has attracted a lot of attentions from the public in recent years. Current studies on automated vehicles mainly focus on microscopic simulations with simple network topologies and driver behaviors, and few has considered to incorporate automated vehicles into macroscopic travel demand models for the analysis in a regional network.

CLR Analytics Inc. received a new Small Business Innovation Research (SBIR) Phase I award titled "Evaluating System Impacts of Automated Vehicles: A Multiple-Resolution Modeling Approach" from the SBIR program of USDOT.

Continued population growth and expanded commercial development have already push the daily traffic on National Park Service (NPS) Parkways exceeds design capacity especially around the urban area. We propose to develop a visually unobtrusive and self-powered traffic monitoring system, NPS Traffic Monitor HD, for vehicle detection and tracking with heterogeneous detection technologies for the NPS Parkways.

This Small Business Innovative Research (SBIR) Phase I project aims to design and develop a Heavy Vehicle Monitoring System that utilize both Inductive Loop Signature Technologies and existing Weigh-In-Motion (WIM).

This FHWA Small Business Innovative Research (SBIR) Phase I project aims to develop a traffic signal alert system that integrates sensory information commonly available on a Smartphone, geographical information of an intersection, and real-time traffic signal information to alert or support decision making for pedestrians and cyclists, who are most vulnerable in vehicle crashes and face more risks when they are crossing intersections.